title: A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics.
abstract: BACKGROUND
  In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.
  
  
  RESULTS
  We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.
  
  
  CONCLUSIONS
  Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.
authors.affiliation: Sysdiag CNRS FRE 3009 BIO-RAD, Cap delta/Parc Euromédecine, 1682 rue de la Valsière, CS 61003, 34184 Montpellier Cedex 4, France. sabine.peres@sysdiag.cnrs.fr
authors.forename: Sabine
authors.initials: S
authors.lastname: Pérès
authors.forename: Laurence
authors.initials: L
authors.lastname: Molina
authors.forename: Nicolas
authors.initials: N
authors.lastname: Salvetat
authors.forename: Claude
authors.initials: C
authors.lastname: Granier
authors.forename: Franck
authors.initials: F
authors.lastname: Molina
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D020543:Proteome